ATINER's Conference Paper Series IND2016-1974ATINER CONFERENCE PAPER SERIES No: IND2016-1974 5...

ATINER CONFERENCE PAPER SERIES No: LNG2014-1176

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Athens Institute for Education and Research

ATINER

ATINER's Conference Paper Series

IND2016-1974

Aslan Deniz Karaoglan

Assistant Professor

Balikesir University

Turkey

Beyazit Ocaktan

Assistant Professor

Balikesir University

Turkey

Abdullah Cicibas

M.Sc. Student

BEST Transformers

Turkey

Flow Time and Due Date Estimation for Customer

Orders According to Design Criteria: A Case Study

of BEST Transformers Company

ATINER CONFERENCE PAPER SERIES No: IND2016-1974

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An Introduction to

ATINER's Conference Paper Series

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organized by our Institute every year. This paper has been peer reviewed by at least two

academic members of ATINER.

Dr. Gregory T. Papanikos

President


This paper should be cited as follows:

Karaoglan, A. D., Ocaktan, B. and Cicibas, A. (2016). "Flow Time and Due

Date Estimation for Customer Orders According to Design Criteria: A Case

Study of BEST Transformers Company", Athens: ATINER'S Conference

Paper Series, No: IND2016-1974.


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ISSN: 2241-2891

13/09/2016


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Flow Time and Due Date Estimation for Customer Orders

According to Design Criteria: A Case Study of BEST

Transformers Company

Aslan Deniz Karaoglan

Beyazit Ocaktan

Abdullah Cicibas

Abstract

In the electro-mechanics industry, the manufacturing is performed by project

type production and most of the sales are performed by tender offers. Because

of project based manufacturing, the most of the transformers are produced for

the first time and the processing times for these orders are unknown. In this

case it is important to estimate the cost of the customer order before the

production at a bidding stage for accurate price offer and win the tender. In this

labor-intensive sector; accurate estimation of the labor cost has great

importance to give a realistic price offer. Therefore, it is important to estimate

the flow time and due date with a low variance. In this study, the core

production process of a transformer company is simulated by using Arena

software, and the technical specifications indicated in the customer orders are

used as simulation inputs for the transformers to be produced for the first time.

Keywords: Due date, Flow shop, Flow time, Simulation, Transformer.

Acknowledgments: This research is supported by Republic of Turkey

Ministry of Science, Industry and Technology [grant number: SANTEZ-

0682.STZ.2014]. The authors would gratefully like to thank BEST

Transformer Research & Development Department for its valuable supports

lead to reveal this paper


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Introduction

In the electromagnetics sector transformers are produced by a labor

intensive project type production system. Except from a few similar

transformers produced frequently; there are unlimited types of different

transformer orders which vary according to the customer specifications those

will be produced the first time. This variation causes different processing

times, a flow time (FT) and due date (DD) for each type of order which has to

be predicted before starting the production.

There are so many studies presented in the literature that estimate the

processing time, FT, and DD by using simulation models, probability

distributions and other statistical techniques and presented remarkable results.

Ekren and Ornek (2008) presented a simulation based experimental design to

analyze factors affecting the production flow time. Baykasoglu and Gokcen

(2009) proposed a new approach that is based on a genetic programming

technique which is known as a gene expression programming (GEP), and

compared the performance of the proposed due date assignment model with

several previously proposed conventional due date assignment models. For this

purpose, simulation models are developed and comparisons of the due date

assignment models are made. Slomp et al. (2011) studied on estimation of flow

times of jobs to be produced in a flexible manufacturing cell (FMC) consisting

of a number of identical machines by the aid of simulation. Joseph and

Sridharan (2011) investigated the effects of dynamic due-date assignment

models (DDDAMs), routing the flexibility levels (RFLs), sequencing

flexibility levels (SFLs) and part sequencing rules (PSRs) on the performance

of a flexible manufacturing system (FMS) for the situation wherein part types

to be produced in the system arrive continuously in a random manner. They

used a discrete-event simulation model of the FMS as a test-bed for

experimentation. Vinod and Sridharan (2011) also presented the salient aspects

of a simulation study conducted to investigate the interaction between due-date

assignment methods and scheduling rules in a typical dynamic job shop

production system. Simulation experiments are carried out for the different

scenarios that arise out of the combination of due-date assignment methods and

scheduling rules. They found that dynamic due-date assignment methods

provide better performance and they developed regression-based

metamodels using the simulation results. Azaron et al. (2011) concerned with

the study of the constant due-date assignment policy in repetitive projects,

where the activity durations are exponentially distributed random variables and

they verified the results by Monte Carlo simulation. Akinnuli et al. (2012)

developed a computer-aided system which is based on a simulation and

empirical model for predicting job-shop FT and DD. Thurer et al. (2012)

evaluated the performance of DD setting rules in the context of complex

product structures by using simulation. In the following year Thurer et al.


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(2013) compared the performances of 11 different DD Setting Rules in Job

Shops with Contingent Orders by the aid of simulation.

Hsieh et al. (2014) proposed a methodology, called progressive simulation

metamodeling (PSM). They used a response surface methodology based

simulation. Lee (2014) consider a problem of estimating order flow times in

two-stage hybrid flow shops, where orders arrive dynamically and various

scheduling schemes can be used. In this study several order flow time

estimation methods are devised and actual flow times of orders are obtained

from simulation runs. Li et al (2016) developed a simulation-based statistical

approach. They provided responsive and high-quality prediction of a new job's

FT through the system, which renders the capability of accurately quoting lead

times in real time. In this approach they integrated an analytical queuing

analysis, design of experiments, and statistical modeling to quantify the

dependence of a new job's FT distribution upon the shop status.

In the literature the common practice to predict the FT is using general job

and shop characteristics such as the number of the job in the queue, processing

times, number of resources, dispatching rules and etc. In addition, it is assumed

that the processing times or its probability distribution are known before

starting the production. However, this assumption is not valid for transformer

production. In this case study, the processing times or its probability

distribution is not known for the orders which will be produced the first time.

This causes problems at giving accurate price offers to the customers because

of unknown processing times and FT of unlimited kind of orders.

In the transformer production, the processing times of operations, the FT

and the DDs of each order vary according to the technical specifications that

are demanded by the customer. In this study, these parameters for each order is

predicted by using an interface software developed by the authors (which is

based on Arena simulation model, statistical modeling and probability

distributions) with the consideration of the technical data instead of using the

general job and shop characteristics. The following section gives a brief

description of the materials and methods used in this study. The case study,

simulation results and the interface software developed for the BEST

Transformers Co. are presented in the “Arena Simulation” section. Finally, the

conclusions are given in the last section.

Materials and Methods

The transformers production process has five main stages: core production,

winding operations, mechanical production, assembly, and final assembly. In

this study, simulation based interface software is developed for estimating the

processing times, FT and DD of the core production process of the

transformers to be produced at the first time. This interface software will be

used by the company to provide data to the sales staff at the bidding stage to

calculate the labor hours, labor cost and prepare better price offering. Also, the


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outcomes of this software will be used for the scheduling of core production of

BEST Transformers Co.

To develop this interface software, the Arena simulation model is created

for the core production of the company. Then, the processing times of labor

intensive processes and preparation times are modeled by using probability

distributions while the standard machining processes are modeled by

mathematical models of design of experiment (DOE) techniques. To run this

simulation a user-friendly interface is coded by using Visual Studio software.

By using this interface, the user enters the technical design parameters to the

program and obtains the simulation results.

Arena Simulation

Simulation is a powerful tool to find good solutions in stochastic

environments. Rosetti (2010) defined the simulation as “a numerical technique

for conducting experiments on a digital computer which involves logical and

mathematical relationships that interact to describe the behavior of a system

over time”. Nowadays, simulation is often used in various fields and industries

by computational power and storage capacity of contemporary computers and

software which are better than ever.

There are many competing languages to conduct a simulation work in a

computer. Some of these languages are specially created for simulation and

uses drag and drop modules like Arena, Promodel, etc. Arena is one of the

most popular simulation software and has input and output analyzer modules.

Moreover, it exploits ActiveX Automation and Visual Basic for Applications

(VBA) for integrating directly with other programs. In this paper, Arena 14

simulation software is used for the simulation.

Response Surface Methodology (RSM)

The design of experiment (DOE) techniques are used for modeling the

relationship between the input variables (factors) and the output variable

(response) by using the minimum number of experimental results. By this way

it is possible to optimize the system parameters or to predict the response of

unpracticed combinations of different factor levels. This requires less time and

effort. The well-known DOE techniques are response surface methodology

(RSM), factorial design and Taguchi. RSM provides the mathematical relations

including interactions between the factors besides the linear and quadratic

relations. Equation (1) shows the general second-order polynomial RSM model

for the experimental design (Montgomery, 2001).

2

0

1 1

n n n

i i ii i ij i j

i i i j

Y X X X X

(1)


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where Y is the response, Xi and Xj are the factors, terms, β0, βi, βii and βij are the

regression coefficients, and is the residual. Equation (1) may be written in

matrix notation as:

Y= β X +ε (2)

In this equation, Y and X represents the output and input matrices

respectively. The residual terms are given by the matrix represented with ε.

The least square estimator of the β matrix that composes of coefficients of the

regression equation (β0, βi, βii and βij) calculated by the given formula in

Equation (3), by ignoring ε;

1

T TX X X Y

(3)

where TX is the transpose of X.

Probability Distributions

The widely used probability distributions in the Arena are Uniform,

Normal, Exponential, Erlang, Gamma, Weibull, Lognormal, Beta, and

Triangular. In this study Erlang, Beta and Triangular distributions best fit the

processing times of labor intensive processes and preparation processes

(Rosetti, 2010). The common modeling situation for Triangular (a,m,b)

distribution assumes a minimum, a maximum and a most likely value. Also,

this distribution roughs model in the absence of data. The probability density

function (F(x)) of Triangular distribution is given in Equation (4-5):

2x a

b a b m

a x m (4)

2

1b x

b a b m

m x b (5)

Where a, b, and m are the minimum, maximum and mode respectively. The

common modeling situation for Erlang distribution is multiple phases of

service with each phase exponential. The probability density function of Erlang

( , r) distribution is given in Equation (6):

/1

0

/1

!

nxr

n

e x

n

(6)


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Where is the mean of each of the component exponential distributions, and r

is the number of exponential random variables. Beta distribution is useful for

modeling task times in a bounded range with little data. The probability density

function of Beta ( 1 , 2 ) is given in Equation (7-8):

11 1

,

x x

0<x<1 (7)

Where is the complete Beta function given by:

1 11

0, = 1t t dt

(8)

Shape parameters Beta ( ) and Alpha ( ) specified as positive real numbers.

The Case Study

BEST Transformers is a transformer producer and produces power

transformers, distribution transformers and dry-type transformers. This study is

performed at a core production of distribution transformers. In this production

oil type and dry type cast resin transformers are produced by flexible flow shop

that is composed of consequent processes. The main production steps are core

production, winding operations, mechanical production, assembly, and final

assembly. The coils produced by separate winding operations are assembled

with the core. Then this mid-product is put in the tank which is produced by

mechanical production. Then, the final operations are performed on this mid-

product and the production is finalized. This study is motivated on core

production of oil type transformers.

In this production system, actually each of the received transformer order

can be accepted as a new product. The same transformers' designs with equal

power and voltage numbers may be completely different because of technical

specification those are demanded by the customers. These design differences

engage accurate prediction of processing times, FT and DD of the orders those

will be produced at first time.

In this study FT estimation of received transformer orders are performed

by using the mathematical equations of RSM, probability distributions and

Arena simulation. RSM was employed by using Minitab program, while

probability distributions and simulation were employed by using Arena

simulation software. In the proposed approach if the probability distribution for

processing time of the product has been defined, it is used at the simulation.

Otherwise, processing times of the products which are produced at the first

time are estimated by regression equations developed according to the product

technical specifications.


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The core production is mainly composed of slicing, cutting and stacking

operations. In slicing operation, steel slice stocks are primarily checked and

ones which are not in stock are sliced in slice cutting machine from the roll in

width set design. In cutting operation, sliced rolls are cut in measurements

determined in design criteria and prepared for stacking operation. Stacking

operation is labor-intensive and the sheets cut are stacked in stacking tools by

workers. In addition, some parts used in stacking operations are produced in

carpenter. The results of the analyzes performed show that carpenter,

preparation and the final completion times don’t change slightly according to

technical characteristics, therefore probability distributions determined by the

Arena input analyzer software are used for these processing times. The

goodness of fit of the distributions is tested by the Chi-square test in 0.05

significance level, and the results show that determined probability

distributions can be used in the simulation. Cutting times are sensitive to

technical specifications according to the result of the analysis. Therefore, a

mathematical equation for cutting times is developed by RSM, and cutting

times generated from mathematical equations are used in the simulation. The

probability distributions and mathematical model used in the simulation are

given at Table 1 in terms of minutes. Transfer distances in the factory among

work stations and stock fields are measured and transfer times of material/

product are determined for the simulation by entering the speed of used

transporters.

Table 1. Processing Time Models in the Simulation

Operation Model

Carpenter Time 10+120*Beta (0.751, 1.31)

Tap Slice Time (20+119*Beta(1.44,1.5))

Cutting Time

(x1:weight, x2:number of sheet)

0.0495443-5.16233*x1+62.8709*x2-

143.455*x1*x1-81.0161*x2*x2 + 230.667*x1*x2

Preparation Time for Tap

Cutting

Triangular (8, 11, 14)

Completion Time for Cutting Triangular (23, 29, 35)

Preparation Time for Stacking Triangular (32, 49, 61)

Limb Stacking Time (6+Erlang(3.69, 2)

Completion Time for Stacking Triangular (30, 40, 50)

The simulation model firstly reads the production order records pending to

be produced from an excel file and simulates these orders by taking in to

account the dynamics conditions of the workshop. The orders whose required

processing time and DD need to be calculated are added current orders records

and simulated.

The created simulation model will be used by both the sales and

production departments. Specially, the sales staff is not familiar to simulation

studies. Therefore, a user-friendly interface is developed for the effective use


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of the simulation in the firm. The developed interface software has two

modules: order analysis for sales department and scenario analysis for the

production department. The screenshot of the interface software is given at

Figure 1.

Figure 1. The Screenshot of the Interface Software

Sales staff enters technical specifications of current order to the simulation

model by the aid of interface software. Thus, processing times, FT and DD can

be estimated by the interface software embedded simulation model at a bidding

stage. On the other hand, Scenario analysis module is used by the production

department. The production department can make a scenario analysis for the

processing time, FT and DD by changing the existing order in simulation.

The 95% confidence intervals for mean of operation times in carpenter,

slicing, cutting and stacking operations, FT and DD for each group in the order

are calculated by simulation as the outputs of interface software. Moreover, the

best and the worst scenarios for FT and DD can be generated according to the

result of replications in simulation and relative probabilities related to the

completion time of each order group can be calculated by simulation as an

output of the software. An example of the output that the interface software

generates is given at Figure 2.

Figure 2. An Example Output of the Interface Software

An order list which includes 11 transformers in 6 groups is chosen for

the verification of outputs. Technical specifications of group orders are

completely different from each other and they cannot be given in this paper

because of the firm's privacy policy. The actual processing times of selected

groups of order are observed and given at Table 2. Simulation model runs

according to the actual order in production and the calculated 95% confidence


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intervals for means of processing times are given at the same table in the terms

of minutes.

Table 2. Observed and Simulated Processing Times

Product

Code Frequency

Observed

Cabinetry

Time

95% Confidence

Interval

For Cabinetry

Time

Observed

Cutting Slice

Time

95% Confidence

Interval

For Cutting Slice

Time

Observed

Cutting

Time

95% Confidence

Interval

For Cutting Time

Observed

Stacking

Time

95% Confidence

Interval

For Stacking

Time

4907892 2 92 90.19 - 114.33 216 198.91 - 215.35 503 495.18 - 497. 61 1154 1152.3 - 1237.8

4907889 1 61 48.31 - 66.10 137 135.4 - 150.60 313 311.02 - 313.11 522 508.32 - 548.96

4907839 1 98 94.73 - 128.80 59 61.10 - 64.10 221 220.50 - 223.03 233 221.96 - 240.27

4906619 5 276 259.58 - 295.01 410 406.44 - 452.31 1153 1151.58 - 1156.94 1980 1960.5 - 2053.4

4906282 1 90 88.42 - 116.92 81 79.45 - 84.91 269 266.27 - 270.47 316 271.55 - 314.23

4907219 1 91 86.16 - 115.09 103 97.312 - 107.03 187 185.06 - 188.11 286 223.94 - 380.75

The observed FT and DD, and simulation results under 95 % confidence

level are calculated and given at Table 3.

Table 3. Observed and Simulated FT and DD

Product

Code Frequency

Observed

Flow Time

95% Confidence Interval

For FlowTime

Observed

Due Date

95% Confidence Interval

For Due Date

4907892 2 2156 2015 - 2199 2908 2900.3 - 2952.9

4907889 1 1285 1121 - 1376 1901 1800.7 - 1915.2

4907839 1 720 685 - 796 1978 1911.0 - 1991.0

4906619 5 4250 4086 - 4580 5320 5102.8 - 5765.6

4906282 1 987 885 - 1112 1820 1733.9 - 1834.1

4907219 1 825 751 - 916 2408 2359.3 - 2591.8

According to the Table 2 and 3, it is clearly indicated that the predicted

simulation results are between the bounds of the confidence interval.

Therefore, it is clear that the simulation model generates successful outputs.

The developed simulation model and interface can be used as decision

support software in the firm for both price offering at bidding stage and

production scheduling.

Conclusions

This study focused on predicting the processing times, FT and DD of the

customer orders those will be produced the first time in a transformer producer.

For this purpose Arena simulation is used. Previously presented studies on this

subject are used general job and shop characteristics and the processing times

are thought to be known before starting the production. The novelty proposed

by this study is using the technical specifications of the orders that are

demanded by the customer directly. In this study, for the labor-intensive

processes, probability distributions are used to predict the processing times

while the processing times of machining processes are predicted by using


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regression equations. Also, interface software is developed. By using this

interface the user enters the technical design parameters to the program and

obtains the simulation results. The results indicate that using the technical

specifications directly gives good results and accurate prediction can be

performed.

References

Akinnuli, B. O., Oluwadare, S. A., and Aderoba, A. A. 2012. A computer-aided

system for job-shop flow time and due-date prediction. Advances in Materials

and Systems Technologies III Book Series: Advanced Materials Research. 367,

(Sep. 2012), 639-645.

Azaron, A., Fynes, B., and Modarres, M. 2011. Due date assignment in repetitive

projects. International Journal of Production Economics. 129, 1 (Jan. 2011), 79-

85. DOI= 10.1016/j.ijpe.2010.09.004.

Baykasoglu, A. and Gocken, M. 2009. Gene expression programming based due

date assignment in a simulated job shop. Expert Systems with Applications. 36, 10

(Dec. 2009), 12143-12150. DOI=10.1016/j.eswa.2009.03.061.

Ekren, B.Y. and Ornek, A.M. 2008. A simulation based experimental design to

analyze factors affecting production flow time. Simulation Modelling Practice

and Theory. 16, 3 (Mar. 2008), 278-293. DOI= 10.1016/j.simpat.2007.11.016.

Joseph, O.A. and Sridharan, R. 2011. Analysis of dynamic due-date assignment

models in a flexible manufacturing system. Journal of Manufacturing Systems.

30, 1 (Jan. 2011) 28-40. DOI= 10.1016/j.jmsy.2011.02.005.

Hsieh, L.Y., Chang, K.H., and Chien, C.F. 2014. Efficient development of cycle time

response surfaces using progressive simulation metamodeling. International

Journal of Production Research. 52, 10 (May 2014), 3097-3109. DOI= 10.1080/

00207543.2013.864055.

Lee, G.C. 2014. Real-time order flow time estimation methods for two-stage hybrid

flowshops, OMEGA-International Journal of Management Science. 42, 1 (Jan.

2014), 1-8. DOI= 10.1016/j.omega.2013.02.004.

Li, M., Yang, F., Wan, H., and et al. 2015. Simulation-based experimental design and

statistical modeling for lead time quotation. Journal of Manufacturing Systems.

37 (Oct. 2015), 362-374. DOI= 10.1016/j.jmsy.2014.07.012.

Montgomery, D.C., 2001. Design and Analysis of Experiments, 5th Edition, John

Wiley &Sons, Inc., New York, NY.

Rosetti, M.D. 2010. Simulation Modeling and Arena. 2nd ed., John Wiley&Sons, Inc.,

Hoboken, New Jersey, NJ.

Slomp, J., Bokhorst, J.A.C., and Caprihan, R. 2011. Average flow time estimation of

jobs in a flexible manufacturing cell consisting of a number of identical

machines. In proceedings of the IEEE International Conference on Industrial

Engineering and Engineering Management (IEEM) (Singapore, December 06-09,

2011). 1426-1430.

Thuerer, M., Huang, G., Stevenson, M., and et al. 2012. The performance of due

date setting rules in assembly and multi-stage job shops: an assessment

by simulation. International Journal of Production Research, 50, 20 (2012),

5949-5965. DOI= 10.1080/00207543.2011.638942.

Thuerer, M., Stevenson, M., Silva, C., and et al. 2013. Towards an integrated

workload control (WLC) concept: the performance of due date setting rules in job


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shops with contingent orders. International Journal of Production Research. 51,

15 (Aug. 2013), 4502-4516. DOI=10.1080/00207543.2013.774485.

Vinod, V. and Sridharan, R. 2011. Simulation modeling and analysis of due-

date assignment methods and scheduling decision rules in a dynamic job shop

production system. International Journal of Production Economics. 129, 1, (Jan.

2011), 127-146. DOI= 10.1016/j.ijpe.2010.08.017.

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